Datasets:
File size: 4,353 Bytes
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license: mit
task_categories:
- time-series-forecasting
tags:
- nigeria
- agriculture
- food-systems
- synthetic
- agricultural-markets-and-pricing
size_categories:
- 100K<n<1M
data_type: synthetic
---
> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.
# Nigeria Agriculture – Commodity Market Prices
## Dataset Description
Daily/weekly market prices by crop, location, volume.
**Category**: Agricultural Markets & Pricing
**Rows**: 180,000
**Format**: CSV, Parquet
**License**: MIT
**Synthetic**: Yes (generated using reference data from FAO, NBS, NiMet, FMARD)
## Dataset Structure
### Schema
- **market**: string
- **commodity**: string
- **date**: string
- **price_ngn_kg**: float
- **volume_kg**: float
### Sample Data
```
| market | commodity | date | price_ngn_kg | volume_kg |
|:---------|:------------|:-----------|---------------:|------------:|
| Dawanau | groundnut | 2023-10-02 | 841.2 | 3900.3 |
| Ariaria | oil_palm | 2023-07-31 | 50 | 4506.9 |
| Dawanau | cocoa | 2022-07-15 | 347.71 | 6406.7 |
| Ariaria | rice | 2023-10-25 | 747.14 | 3621.6 |
| Mile 12 | sorghum | 2022-07-18 | 650.7 | 2689.1 |
```
## Data Generation Methodology
This dataset was synthetically generated using:
1. **Reference Sources**:
- FAO (Food and Agriculture Organization) - crop yields, production data
- NBS (National Bureau of Statistics, Nigeria) - farm characteristics, surveys
- NiMet (Nigerian Meteorological Agency) - weather patterns
- FMARD (Federal Ministry of Agriculture and Rural Development) - extension guides
- IITA (International Institute of Tropical Agriculture) - agronomic research
2. **Domain Constraints**:
- Crop calendars and phenology (planting/harvest windows)
- Agro-ecological zone characteristics (Sahel, Sudan Savanna, Guinea Savanna, Rainforest)
- Nigeria-specific realities (smallholder dominance, market dynamics, conflict zones)
- Statistical distributions matching national agricultural patterns
3. **Quality Assurance**:
- Distribution testing (KS test, chi-square)
- Correlation validation (rainfall-yield, fertilizer-yield, yield-price)
- Causal consistency (DAG-based generation)
- Multi-scale coherence (farm → state aggregations)
- Ethical considerations (representative, unbiased)
See `QUALITY_ASSURANCE.md` in the repository for full methodology.
## Use Cases
- **Machine Learning**: Yield prediction, price forecasting, pest detection, supply chain optimization
- **Policy Analysis**: Agricultural program evaluation, subsidy impact assessment, food security planning
- **Research**: Climate-agriculture interactions, market dynamics, technology adoption patterns
- **Education**: Teaching agricultural economics, data science applications in agriculture
## Limitations
- **Synthetic data**: While grounded in real distributions, individual records are not real observations
- **Simplified dynamics**: Some complex interactions (e.g., multi-generational pest populations) are simplified
- **Temporal scope**: Covers 2022-2025; may not reflect longer-term trends or future climate scenarios
- **Spatial resolution**: State/LGA level; does not capture micro-level heterogeneity within localities
## Citation
If you use this dataset, please cite:
```bibtex
@dataset{nigeria_agriculture_2025,
title = {Nigeria Agriculture – Commodity Market Prices},
author = {Electric Sheep Africa},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/electricsheepafrica/nigerian_agriculture_commodity_market_prices}
}
```
## Related Datasets
This dataset is part of the **Nigeria Agriculture & Food Systems** collection:
- https://huggingface.co/collections/electricsheepafrica/nigeria-agriculture-and-food-systems
## Contact
For questions, feedback, or collaboration:
- **Organization**: Electric Sheep Africa
- **Collection**: Nigeria Agriculture & Food Systems
- **Repository**: https://github.com/electricsheepafrica/nigerian-datasets
## Changelog
### Version 1.0.0 (October 2025)
- Initial release
- 180,000 synthetic records
- Quality-assured using FAO/NBS/NiMet reference data
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